82 citations · 486 across the 40 of their papers we have counts for
7 papers · 1 filter
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Hongjin Su, Weijia Shi, Jungo Kasai +7
We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g.,…
Selective Annotation Makes Language Models Better Few-Shot Learners
Hongjin Su, Jungo Kasai, Chen Henry Wu +8
Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they l…
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes
Sitong Zhou, Kevin Lybarger, Meliha Yetisgen +1
Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extractio…
INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions
Zeqiu Wu, Ryu Parish, Hao Cheng +4
In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types acco…
Unsupervised Learning of Hierarchical Conversation Structure
Bo-Ru Lu, Yushi Hu, Hao Cheng +2
Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dial…
Automatic Dialect Density Estimation for African American English
Alexander Johnson, Kevin Everson, Vijay Ravi +3
In this paper, we explore automatic prediction of dialect density of the African American English (AAE) dialect, where dialect density is defined as the percentage of words in an u…
In-Context Learning for Few-Shot Dialogue State Tracking
Yushi Hu, Chia-Hsuan Lee, Tianbao Xie +3
Collecting and annotating task-oriented dialogues is time-consuming and costly; thus, zero and few shot learning could greatly benefit dialogue state tracking (DST). In this work,…